TY - JOUR
T1 - Linguistic analysis for emotion recognition
T2 - a case of Chinese speakers
AU - Schirru, Carlo
AU - Simin, Shahla
AU - Mengoni, Paolo
AU - Milani, Alfredo
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive licence to Springer Science Business Media, LLC, part of Springer Nature
PY - 2023/7
Y1 - 2023/7
N2 - Acoustic features, in the examination of emotions occurring in pronouncing English and Chinese Mandarin words, are investigated in this study, then different emotion recognition experiments are presented. To this end, the sound recordings for 91 speakers were analyzed. In the test experiment, a linguistic data set was used to examine which acoustic features are most important for the emotional representation in signal acquisition, segmentation, construction, and encoding. In doing so, words, syllables, phonemes (which contain vowels and consonants), stress and frequency tones were taken into consideration. The types of emotions considered in the experiment included neutral, happy, and sad. Time duration differences, F0 frequency, and dB intensity levels variables were used in conjunction with unsupervised and supervised machine learning approaches for emotion recognition.
AB - Acoustic features, in the examination of emotions occurring in pronouncing English and Chinese Mandarin words, are investigated in this study, then different emotion recognition experiments are presented. To this end, the sound recordings for 91 speakers were analyzed. In the test experiment, a linguistic data set was used to examine which acoustic features are most important for the emotional representation in signal acquisition, segmentation, construction, and encoding. In doing so, words, syllables, phonemes (which contain vowels and consonants), stress and frequency tones were taken into consideration. The types of emotions considered in the experiment included neutral, happy, and sad. Time duration differences, F0 frequency, and dB intensity levels variables were used in conjunction with unsupervised and supervised machine learning approaches for emotion recognition.
UR - http://www.scopus.com/inward/record.url?scp=85150249216&partnerID=8YFLogxK
U2 - 10.1007/s10772-023-10028-x
DO - 10.1007/s10772-023-10028-x
M3 - Journal article
SN - 1381-2416
VL - 26
SP - 417
EP - 432
JO - International Journal of Speech Technology
JF - International Journal of Speech Technology
IS - 2
ER -